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Unified probabilistic models for face recognition from a single example image per person

  • Pattern Recognition and Image Processing
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Abstract

This paper presents a new technique of unified probabilistic models for face recognition from only one single example image per person. The unified models, trained on an obtained training set with multiple samples per person, are used to recognize facial images from another disjoint database with a single sample per person. Variations between facial images are modeled as two unified probabilistic models: within-class variations and between-class variations. Gaussian Mixture Models are used to approximate the distributions of the two variations and exploit a classifier combination method to improve the performance. Extensive experimental results on the ORL face database and the authors' database (the ICT-JDL database) including totally 1,750 facial images of 350 individuals demonstrate that the proposed technique, compared with traditional eigenface method and some well-known traditional algorithms, is a significantly more effective and robust approach, for face recognition.

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References

  1. Wiskott Laurenz, Fellous Jean-Marc, Norbert Krugeret al., Face recognition by elastic bunch graph matching.IEEE Trans. Pattern Analysis and Machine Intelligence, 1997, 19(7): 775–779.

    Article  Google Scholar 

  2. Cootes T F, Taylor C J. Active shape model search using local grey-level models: A quantitative evaluation.British Machine Vision Conference, 1993.

  3. Lanitis A, Taylor C J, Cootes T F, Automatic interpretation and coding of face images using flexible models.IEEE Trans. Pattern Analysis and Machine Intelligence, 1997, 19(7): 743–756.

    Article  Google Scholar 

  4. Cootes T F, Edwards G J, Taylor C J. Active appearance models.IEEE European Conf. Computer Vision, 1998.

  5. Penev P, Atick J. Local feature analysis: A general statistical theory for object representation.Network: Computation in Neural Systems, 1996, 7(3): 477–500.

    Article  MATH  Google Scholar 

  6. Turk M, Pentland A. Eigenfaces for recognition.Journal of Cognitive Neuroscience, 1991, 3(1): 71–86.

    Article  Google Scholar 

  7. Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection.IEEE Trans. Pattern Analysis and Machine Intelligence, 1997, 19(7): 711–720.

    Article  Google Scholar 

  8. Swets D L, Weng J. Using discriminant eigenfeatures for image retrieval.IEEE Trans. Pattern Analysis and Machine Intelligence, 1996, 18(8): 831–836.

    Article  Google Scholar 

  9. Etemad K, Chellappa R. Discriminant analysis for recognition of human face images.Journal of the Optical Society of America, 1997, 14: 1723–1733

    Article  Google Scholar 

  10. Martinez A M, Kak A C. PCA versus LDA.IEEE Trans. Pattern Analysis and Machine Intelligence, 2001, 23(2): 228–233.

    Article  Google Scholar 

  11. Philips P Jonathon. Support Vector Machines Applied to Face Recognition. InAdvances in Neural Information Processing Systems II, Kearns M J, Solla S A, Cohn D A (eds.), MIT Press, 1999.

  12. Li Y, Gong S, Liddell H. Support vector regression and classification based multi-view face detection and recognition.IEEE International Conference on Automatic Face and Gesture Recognition, 2000.

  13. Johnsson K, Kittler J, Li Y, Matas J. Support vector machines for face authentication.British Machine Vision Conference, 1999.

  14. Liu Qingshan, Huang Rui, Lu Hanqinget al. Face recognition using kernel based Fisher discriminant analysis.IEEE International Conference on Automatic Face and Gesture Recognition, 2002.

  15. Yang Ming-Hsuan, Ahuja Narendra, Kriegman David. Face recognition using kernel eigenfaces.IEEE International Conference on Image Processing, 2000.

  16. Yang Ming-Hsuan. Kernel eigenfaces vs. kernel fisherfaces: Face recognition using kernel methods.IEEE International Conference on Automatic Face and Gesture Recognition, 2002.

  17. Bartlett M, Sejnowski T. Independent components of face images: A representation for face recognition.Annual Joint Symposium on Neural Computation, 1997.

  18. Lawrence Steve, Yianilos Peter, Cox Ingemar. Face recognition using mixture-distance and raw images.IEEE Int. Conf. Systems, Man, and Cybernetics, 1997.

  19. Moghanddam Baback, Pentland Alex. Probabilistic visual learning for object detection.IEEE International Conference on Computer Vision, 1995.

  20. Gross Ralph, Yang Jie, Walbel Alex. Growing Gaussian mixture models for pose invariant face recognition.International Conference on Pattern Recognition, 2000.

  21. Er Meng Joo, Wu Shiqian, Lu Juweiet al. Face recognition with radial basis function (RBF) neural networks.IEEE Trans. Neural Netowrks, 2002, 13(3): 697–710.

    Article  Google Scholar 

  22. Dai Ying, Nakano Yasuaki: Recognition of facial images with low resolution using a Hopfield memory model.Pattern Recognition, 1998, 31(2): 159–167.

    Article  Google Scholar 

  23. Huang Fu Jie, Zhou Zhihua, Zhang Hong-Jianget al. Pose invariant face recognition.IEEE Int. Conf. Automatic Face and Gesture Recognition, 2000.

  24. Zhou Zhihua, Huang Fu Jie, Zhang Hong-Jianget al. View-invariant face recognition based on neural network ensemble.Journal of Computer Research and Development, 2001, 38(10): 1204–1210. (in Chinese)

    Google Scholar 

  25. Liu Chengjun, Wechsler Harry. Evolutionary pursuit and its application to face recognition.IEEE Trans. Pattern Analysis and Machine Intelligence., 2000, 22(6): 570–582.

    Article  Google Scholar 

  26. Liu Chengjun, Wechsler Harry. Probabilistic reasoning models for face recognition.IEEE Computer Society Conf. Computer Vision and Pattern Recognition, 1998.

  27. Wu Xiaojun, Yang Jingyu, Wang Shitonget al. A new algorithm for generalized optimal discriminant vectors.J. Computer Science and Technology, 2002, 17(3): 324–330.

    Article  MATH  Google Scholar 

  28. Guo Yuefei, Yang Jingyu. An iterative algorithm for the generalized optimal set of discriminant vectors and its application to face recognition.Chinese Journal of Computer, 2000, 23(11): 1189–1195. (in Chinese)

    Google Scholar 

  29. Lai Jian Huang, Yuen P C, Feng G C. Face recognition using holistic Fourier invariant features.Pattern. Recognition, 2001, 34(1): 95–109.

    Article  MATH  Google Scholar 

  30. Li S Z, Lu J. Face recognition using the nearest feature line method.IEEE Trans. Neural Networks, 1999, 10(2): 439–443.

    Article  Google Scholar 

  31. Samaria F, Fallside F. Face identification and feature extraction using Hidden Markov Models.Image Processing: Theory and Applications, Vernazza G, Elsevier, San Remo (eds.), Italy, June 1993.

  32. Liu Yi-Guang, Shen Li. Face image location using Hausdorff distance.Journal of Computer Research and Development, 2001, 38(4): 1204–1210. (in Chinese).

    Google Scholar 

  33. Xiong Zhi-Yong, Shen Li. A general face image recognition system based on dual attribute graph.Chinese J. Computer, 2001, 24(7): 764–769. (in Chinese)

    Google Scholar 

  34. Moghaddam Baback, Jebara Tony, Pentland Alex. Bayesian face recognition.Pattern Recognition, 2000, 33(11): 1771–1782.

    Article  Google Scholar 

  35. Philips P J, Wechsler H, Huang J Set al. The FERET database and evaluation procedure for face-recognition algorithms.Image and Vision Computing, 1998, 16(5): 295–306.

    Article  Google Scholar 

  36. Bishop C M. Neural Networks for Pattern Recognition. New York: Oxford University Press, 1995.

    Google Scholar 

  37. Dempster A P, Laird N M, Rubin D B. Maximum likelihood from incomplete data via the EM algorithm.Journal of the Royal Statistical Society, 1977, B 39(1): 1–38.

    MATH  MathSciNet  Google Scholar 

  38. Kittler J, Hatef M, Duin R P W, Matas J. On combining classifiers.IEEE Trans. Pattern Analysis and Machine Intelligence, 1998, 20(3): 226–239.

    Article  Google Scholar 

  39. Tax D M J, Breukelen M, Duin R P W, Kittler J. Combining multiple classifiers by averaging or by multiplying?Pattern Recognition, 2000, 33(9): 1475–1485.

    Article  Google Scholar 

  40. Olivetti & Oracle Research Laboratory. The Olivetti & Oracle Research Laboratory Face Database, http://www.uk.research.att.com/facedatabase.html.

  41. Lee Te-Won, Lewicki M S, Sejnowski T J. ICA mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind signal separation.IEEE Trans. Pattern Analysis and Machine Intelligence, 2000, 22(10): 1078–1089.

    Article  Google Scholar 

  42. Roberts S J, Penny W D. Mixtures of independent component analysers.Int. Conf. Artificial Neural Networks, 2001.

  43. Choudrey R A, Robers S J. Variation mixture of Bayesian independent component analysers.Technical Report, 2001, http://www.robots.ox.ac.uk/~parg/publications.html.

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Authors and Affiliations

  1. Institute of Computing Technology, The Chinese Academy of Sciences, 100080, Beijing, P.R. China

    Pin Liao & Li Shen

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  1. Pin Liao
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  2. Li Shen
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Correspondence to Pin Liao.

Additional information

Regular Paper

This work is supported partly by the National Natural Science Foundation of China (Grant No.69789301), National Hi-Tech R&D 863 Program of China (Grant No2001AA114190), and Sichuan Chengdu Yinchen Net. Co. (YCNC).

Pin Liao received the B.S. degree in computer science from Nanchang University, Nanchang, P.R. China, in 1996 and the M.S. degree in pattern recognition and intelligent system from beijing Institute of Technology, Beijing, P.R. China, in 1999. He is currently a Ph.D. candidate in Institute of Computing Technology, the Chinese Academy of Sciences. His current research interests include pattern recognition, computer vision, and neural networks.

Li Shen was born in Zhejiang, China, 1937. He graduated from the Department of Electrical Engineering, Zhejiang University, China, in 1959. Since then, he joined the staff of Institute of Computing Technology, the Chinese Academy of Sciences, where he is currently a professor. He is now an IEEE senior member. His research interests include soft computing, ASIC design, design for testability, and fault testing.

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Liao, P., Shen, L. Unified probabilistic models for face recognition from a single example image per person. J. Comput. Sci. & Technol. 19, 383–392 (2004). https://doi.org/10.1007/BF02944908

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  • DOI: https://doi.org/10.1007/BF02944908

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